Model Card for schaeff/gpt2-small_vanilla300

Associated publication: Transformers Don’t Need LayerNorm at Inference Time: Scaling LayerNorm Removal to GPT-2 XL and the Implications for Mechanistic Interpretability (arXiv TBD)

Associated GitHub: removing-layer-norm

This model is based on openai-community/gpt2 and was finetuned on OpenWebText for 300 iterations with 0.5M tokens per iteration. This model has the same architecture as the corresponding gpt-2 model and is being made available for reproducibility of the results reported in the associated publication.

Usage

You can load the model with transformers:

model = GPT2LMHeadModel.from_pretrained("schaeff/gpt2-small_vanilla300")

Model Collection

This model is part of a collection of LayerNorm-free models. The table below provides links and details.

Evaluation results of LN-free, vanilla fine-tuned, and original GPT-2 models

Reported values are mean cross-entropy losses for 10.2M tokens for The Pile and The Pile filtered and 4.5M tokens for the OpenWebText (WT) validation set. For each model size and dataset, the lowest loss is highlighted in bold, and the loss difference between the LN-free model and the best-performing model is shown in brackets.

Footnotes:

1. GPT-2 XL original: Median: 1.0103, 95 Percentile Range: [0.0005, 10.6193], 99.9% Percentile Range [≈0.0000, 43.0064]
2. GPT-2 XL LN-free: Median: 1.0937, 95 Percentile Range: [0.0004, 10.7548], 99.9% Percentile Range [≈0.0000, 48.6459]

Citation

If you have found our work useful please cite as:

@misc{gpt2layernorm2025,
  author = {Baroni, Luca and Khara, Galvin and Schaeffer, Joachim and Subkhankulov, Marat and Heimersheim, Stefan},
  title = {Transformers Don't Need LayerNorm at Inference Time: Scaling LayerNorm Removal to GPT-2 XL and the Implications for Mechanistic Interpretability},
  year = {2025},
  eprint = {2507.02559},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  url = {https://arxiv.org/abs/2507.02559v1}
}